9-NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ready-saturn",
   "metadata": {},
   "source": [
    "## Natural Language Processing\n",
    "\n",
    "In this notebook, we'll try out some NLP techniques on a sentiment analysis task for IMDB movie reviews.\n",
    "\n",
    "Inspired by: https://www.kaggle.com/jishapjoseph/imdb-sentiment-analysis-v1-tfidf-word2vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "emerging-bookmark",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\eirik\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to\n",
      "[nltk_data]     C:\\Users\\eirik\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\eirik\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "# First, let's import the necessary libraries\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import string\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import nltk\n",
    "nltk.download('stopwords')\n",
    "nltk.download('wordnet')\n",
    "nltk.download('punkt')\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from nltk.corpus import stopwords, wordnet\n",
    "from gensim.models import Word2Vec\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "rubber-toilet",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import dataset from csv-file using Pandas, and storing as a Dataframe\n",
    "# The dataset can be found open on Kaggle: https://www.kaggle.com/columbine/imdb-dataset-sentiment-analysis-in-csv-format\n",
    "\n",
    "df = pd.read_csv('datasets/IMDB_reviews_train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ceramic-accuracy",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I grew up (b. 1965) watching and loving the Th...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>When I put this movie in my DVD player, and sa...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Why do people who do not know what a particula...</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Even though I have great interest in Biblical ...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Im a die hard Dads Army fan and nothing will e...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A terrible movie as everyone has said. What ma...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Finally watched this shocking movie last night...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I caught this film on AZN on cable. It sounded...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>It may be the remake of 1987 Autumn's Tale aft...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>My Super Ex Girlfriend turned out to be a plea...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label\n",
       "0  I grew up (b. 1965) watching and loving the Th...      0\n",
       "1  When I put this movie in my DVD player, and sa...      0\n",
       "2  Why do people who do not know what a particula...      0\n",
       "3  Even though I have great interest in Biblical ...      0\n",
       "4  Im a die hard Dads Army fan and nothing will e...      1\n",
       "5  A terrible movie as everyone has said. What ma...      0\n",
       "6  Finally watched this shocking movie last night...      1\n",
       "7  I caught this film on AZN on cable. It sounded...      0\n",
       "8  It may be the remake of 1987 Autumn's Tale aft...      1\n",
       "9  My Super Ex Girlfriend turned out to be a plea...      1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quick overview of the dataset \n",
    "# Label 0 means negative, while label 1 means positive\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "arbitrary-surgery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I grew up (b. 1965) watching and loving the Thunderbirds. All my mates at school watched. We played \"Thunderbirds\" before school, during lunch and after school. We all wanted to be Virgil or Scott. No one wanted to be Alan. Counting down from 5 became an art form. I took my children to see the movie hoping they would get a glimpse of what I loved as a child. How bitterly disappointing. The only high point was the snappy theme tune. Not that it could compare with the original score of the Thunderbirds. Thankfully early Saturday mornings one television channel still plays reruns of the series Gerry Anderson and his wife created. Jonatha Frakes should hand in his directors chair, his version was completely hopeless. A waste of film. Utter rubbish. A CGI remake may be acceptable but replacing marionettes with Homo sapiens subsp. sapiens was a huge error of judgment.\n"
     ]
    }
   ],
   "source": [
    "# The first review is negative, here is the text\n",
    "print(df[\"text\"][0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "actual-people",
   "metadata": {},
   "source": [
    "## Data analysis and preprocessing\n",
    "First we will inspect the dataset and do some preprocessing to it.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "coupled-generator",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, we'll copy the original dataset to compare with the preprocessed one later\n",
    "df_original = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "distributed-groove",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>I grew up (b. 1965) watching and loving the Th...</td>\n",
       "      <td>0</td>\n",
       "      <td>874</td>\n",
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       "      <th>1</th>\n",
       "      <td>When I put this movie in my DVD player, and sa...</td>\n",
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       "      <th>2</th>\n",
       "      <td>Why do people who do not know what a particula...</td>\n",
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       "      <td>983</td>\n",
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       "      <th>3</th>\n",
       "      <td>Even though I have great interest in Biblical ...</td>\n",
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       "      <th>4</th>\n",
       "      <td>Im a die hard Dads Army fan and nothing will e...</td>\n",
       "      <td>1</td>\n",
       "      <td>983</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A terrible movie as everyone has said. What ma...</td>\n",
       "      <td>0</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Finally watched this shocking movie last night...</td>\n",
       "      <td>1</td>\n",
       "      <td>1377</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>I caught this film on AZN on cable. It sounded...</td>\n",
       "      <td>0</td>\n",
       "      <td>1489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>It may be the remake of 1987 Autumn's Tale aft...</td>\n",
       "      <td>1</td>\n",
       "      <td>1137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>My Super Ex Girlfriend turned out to be a plea...</td>\n",
       "      <td>1</td>\n",
       "      <td>1468</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                                text  label  word_count\n",
       "0  I grew up (b. 1965) watching and loving the Th...      0         874\n",
       "1  When I put this movie in my DVD player, and sa...      0        1811\n",
       "2  Why do people who do not know what a particula...      0         983\n",
       "3  Even though I have great interest in Biblical ...      0         351\n",
       "4  Im a die hard Dads Army fan and nothing will e...      1         983\n",
       "5  A terrible movie as everyone has said. What ma...      0         596\n",
       "6  Finally watched this shocking movie last night...      1        1377\n",
       "7  I caught this film on AZN on cable. It sounded...      0        1489\n",
       "8  It may be the remake of 1987 Autumn's Tale aft...      1        1137\n",
       "9  My Super Ex Girlfriend turned out to be a plea...      1        1468"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get word count for dataset\n",
    "df['word_count'] = df['text'].str.lower().str.len()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "mathematical-examination",
   "metadata": {
    "scrolled": true
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Word Count Distribution')"
      ]
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show distribution of word counts\n",
    "sns.histplot(data=df, x=\"word_count\").set_title('Word Count Distribution')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "weighted-doctrine",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Method to remove stopwords, remove puncation, make text lowercase and update wordcount\n",
    "\n",
    "def preprocess_text(data):\n",
    "    stop = stopwords.words('english')\n",
    "    punct = string.punctuation\n",
    "    #Make lower\n",
    "    data['text'] = data['text'].str.lower()\n",
    "    #Remove stopwords\n",
    "    data['text'] = data['text'].apply(lambda x: ' '.join([words for words in x.split() if words not in stop]))\n",
    "    #Remove punctuations\n",
    "    data['text'] = data['text'].str.translate(str.maketrans('', '', punct))\n",
    "    data['word_count'] = data['text'].str.split().str.len()\n",
    "    return data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "muslim-carol",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>grew b 1965 watching loving thunderbirds mates...</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>put movie dvd player sat coke chips expectatio...</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>people know particular time past like feel nee...</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>even though great interest biblical movies bor...</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>im die hard dads army fan nothing ever change ...</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label  word_count\n",
       "0  grew b 1965 watching loving thunderbirds mates...      0          87\n",
       "1  put movie dvd player sat coke chips expectatio...      0         162\n",
       "2  people know particular time past like feel nee...      0          99\n",
       "3  even though great interest biblical movies bor...      0          33\n",
       "4  im die hard dads army fan nothing ever change ...      1         109"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = preprocess_text(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "miniature-walter",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grew b 1965 watching loving thunderbirds mates school watched played thunderbirds school lunch school wanted virgil scott one wanted alan counting 5 became art form took children see movie hoping would get glimpse loved child bitterly disappointing high point snappy theme tune could compare original score thunderbirds thankfully early saturday mornings one television channel still plays reruns series gerry anderson wife created jonatha frakes hand directors chair version completely hopeless waste film utter rubbish cgi remake may acceptable replacing marionettes homo sapiens subsp sapiens huge error judgment\n"
     ]
    }
   ],
   "source": [
    "print(df['text'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "baking-james",
   "metadata": {},
   "outputs": [],
   "source": [
    "def lemmatize(data):\n",
    "    lemmatizer = WordNetLemmatizer()\n",
    "    data['text'] = data['text'].apply(lambda x: ' '.join([lemmatizer.lemmatize(w) for w in word_tokenize(x)]))\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "unusual-disabled",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "      <th>word_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>grew b 1965 watching loving thunderbird mate s...</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>put movie dvd player sat coke chip expectation...</td>\n",
       "      <td>0</td>\n",
       "      <td>162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>people know particular time past like feel nee...</td>\n",
       "      <td>0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>even though great interest biblical movie bore...</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>im die hard dad army fan nothing ever change t...</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text  label  word_count\n",
       "0  grew b 1965 watching loving thunderbird mate s...      0          87\n",
       "1  put movie dvd player sat coke chip expectation...      0         162\n",
       "2  people know particular time past like feel nee...      0          99\n",
       "3  even though great interest biblical movie bore...      0          33\n",
       "4  im die hard dad army fan nothing ever change t...      1         109"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = lemmatize(df)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "advance-colon",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "grew b 1965 watching loving thunderbird mate school watched played thunderbird school lunch school wanted virgil scott one wanted alan counting 5 became art form took child see movie hoping would get glimpse loved child bitterly disappointing high point snappy theme tune could compare original score thunderbird thankfully early saturday morning one television channel still play rerun series gerry anderson wife created jonatha frakes hand director chair version completely hopeless waste film utter rubbish cgi remake may acceptable replacing marionette homo sapiens subsp sapiens huge error judgment\n"
     ]
    }
   ],
   "source": [
    "print(df['text'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pointed-gasoline",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>grew b 1965 watching loving thunderbird mate s...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>put movie dvd player sat coke chip expectation...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>people know particular time past like feel nee...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>even though great interest biblical movie bore...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>im die hard dad army fan nothing ever change t...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39995</th>\n",
       "      <td>western union something forgotten classic west...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39996</th>\n",
       "      <td>movie incredible piece work explores every noo...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39997</th>\n",
       "      <td>wife watched movie plan visit sicily stromboli...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39998</th>\n",
       "      <td>first watched flatliners amazed necessary feat...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39999</th>\n",
       "      <td>would film good gross estimated 95000000 award...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>40000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    text  label\n",
       "0      grew b 1965 watching loving thunderbird mate s...      0\n",
       "1      put movie dvd player sat coke chip expectation...      0\n",
       "2      people know particular time past like feel nee...      0\n",
       "3      even though great interest biblical movie bore...      0\n",
       "4      im die hard dad army fan nothing ever change t...      1\n",
       "...                                                  ...    ...\n",
       "39995  western union something forgotten classic west...      1\n",
       "39996  movie incredible piece work explores every noo...      1\n",
       "39997  wife watched movie plan visit sicily stromboli...      0\n",
       "39998  first watched flatliners amazed necessary feat...      1\n",
       "39999  would film good gross estimated 95000000 award...      1\n",
       "\n",
       "[40000 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove word_count column, as it may not give us any imporant information for the ML part\n",
    "df = df[['text','label']]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swedish-member",
   "metadata": {},
   "source": [
    "## Creating features\n",
    "We will make the text understandable for ML models by using n-grams (unigrams and bigrams) and taking the TF-IDF score of these n-grams. We will also try word embeddings to see if that gives better results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "inclusive-charter",
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_converter = TfidfVectorizer(max_features=1000, ngram_range=(1,2))\n",
    "X = tfidf_converter.fit_transform(df['text']).toarray()\n",
    "y = df['label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "subsequent-strain",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(40000, 1000)\n",
      "[0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.15247896 0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.17275051 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.18280819 0.         0.         0.\n",
      " 0.         0.         0.27084689 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.13650833 0.         0.\n",
      " 0.         0.         0.         0.09056747 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.10812936 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.14415118 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.05700817 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.16565886 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.07774417 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.141375   0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.1364248  0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.15881145 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.14576549\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.1191726  0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.05311725 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.11520695\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.1256772  0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.11124132 0.12789015 0.\n",
      " 0.         0.         0.         0.         0.         0.11495041\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.18112403\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.44007586 0.         0.15534533 0.\n",
      " 0.         0.         0.         0.         0.         0.07745879\n",
      " 0.16791756 0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.13272418 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.10126994\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.16804905 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.15588499 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.15377241 0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.14115286\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.29536041 0.         0.14510769 0.         0.         0.\n",
      " 0.         0.12955842 0.10632269 0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.14010801\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.07864839 0.         0.         0.         0.\n",
      " 0.         0.         0.         0.         0.         0.\n",
      " 0.         0.         0.         0.        ]\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(X[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "seven-hindu",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['vampire', 'ghost', 'disney', 'zombie', 'match', 'joe', 'japanese', 'train', 'cat', 'italian', 'alien', 'band', 'german', 'gay', 'lee', 'creature', 'island', 'de', 'cartoon', 'wood', 'teen', 'high school', 'political', 'spirit', 'chase', 'attack', 'color', 'fighting', 'season', 'animal', 'master', 'bill', 'player', 'soldier', 'channel', 'brain', 'fantasy', 'bad guy', 'award', 'married', 'william', 'cute', 'dr', 'dumb', 'footage', 'tension', 'animation', 'remake', 'shoot', 'public', 'baby', 'la', 'new york', 'mistake', 'clever', 'scifi', 'concept', 'memory', 'laughing', 'western', 'creepy', 'odd', 'pretty good', 'throw', 'store', 'us', 'win', 'missed', 'potential', 'tom', 'talented', 'manages', 'telling', 'dance', 'doctor', 'air', 'fear', 'familiar', 'list', 'adventure', 'costume', 'appearance', 'pure', 'company', 'project', 'while', 'ended', 'fails', 'rich', 'wasted', 'feel like', 'fire', 'pace', 'water', 'sexual', 'lover', 'pretty much', 'large', 'ability', 'one thing'])\n"
     ]
    }
   ],
   "source": [
    "# Display top-20 tf-idf words\n",
    "feature_array = tfidf_converter.get_feature_names()\n",
    "tfidf_sorting = tfidf_converter.idf_\n",
    "\n",
    "tuples = list(zip(feature_array, tfidf_sorting))\n",
    "sorted_unigrams = sorted(tuples, key=lambda x: x[1], reverse=True)\n",
    "print(dict(sorted_unigrams[:100]).keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "practical-spending",
   "metadata": {},
   "source": [
    "## Train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "theoretical-welding",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train and test set, based on a 80/20 stratified split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.8, random_state=42, stratify=df['label'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "hawaiian-length",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We make a function for how to train the models, so we don't have to rewrite the same code more than once\n",
    "\n",
    "def train_model(model, X_train, y_train, X_test):\n",
    "    model.fit(X_train, y_train)\n",
    "    predictions = model.predict(X_test)\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "combined-tennis",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We store an array of models to run through the function from last codeblock.\n",
    "# They are initialized with default paramters, except for random_state and max_iterations for LR \n",
    "random_seed = 42\n",
    "models = [LogisticRegression(random_state=random_seed, max_iter=500),\n",
    "          LinearSVC(random_state=random_seed),\n",
    "          MultinomialNB()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "surgical-uniform",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Report for LogisticRegression:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.859     0.852     0.856      4004\n",
      "           1      0.853     0.860     0.857      3996\n",
      "\n",
      "    accuracy                          0.856      8000\n",
      "   macro avg      0.856     0.856     0.856      8000\n",
      "weighted avg      0.856     0.856     0.856      8000\n",
      "\n",
      "Report for LinearSVC:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.858     0.852     0.855      4004\n",
      "           1      0.852     0.859     0.856      3996\n",
      "\n",
      "    accuracy                          0.855      8000\n",
      "   macro avg      0.855     0.855     0.855      8000\n",
      "weighted avg      0.855     0.855     0.855      8000\n",
      "\n",
      "Report for MultinomialNB:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0      0.842     0.816     0.829      4004\n",
      "           1      0.822     0.847     0.834      3996\n",
      "\n",
      "    accuracy                          0.832      8000\n",
      "   macro avg      0.832     0.832     0.832      8000\n",
      "weighted avg      0.832     0.832     0.832      8000\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for model in models:\n",
    "    predictions = train_model(model, X_train, y_train, X_test)\n",
    "    c_report = classification_report(y_test, predictions, digits=3)\n",
    "    print(f\"Report for {type(model).__name__}:\")\n",
    "    print(c_report)\n",
    "    \n",
    "    # Show confusion matrix for each classifier\n",
    "    conf_mat = confusion_matrix(y_test, predictions)\n",
    "    cmd = ConfusionMatrixDisplay(conf_mat, display_labels=['Negative','Positive'])\n",
    "    cmd.plot(cmap='Blues')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "chicken-liabilities",
   "metadata": {},
   "outputs": [],
   "source": [
    "#X_original = tfidf_converter.fit_transform(df_original['text']).toarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "floppy-master",
   "metadata": {},
   "source": [
    "## Using word2vec embeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "removable-brake",
   "metadata": {},
   "outputs": [],
   "source": [
    "sentences = pd.Series(df['text']).values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "piano-terminal",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Word2Vec(size=50, window=4, min_count=1, workers=4, sg=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "monetary-particle",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.build_vocab(sentences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "hindu-afternoon",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(118347632, 675989880)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.train(sentences, total_examples=model.corpus_count, epochs=20, report_delay=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "nonprofit-prerequisite",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"word 'movie' not in vocabulary\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-38-3b239f37ec27>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwv\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmost_similar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpositive\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"movie\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\gensim\\models\\keyedvectors.py\u001b[0m in \u001b[0;36mmost_similar\u001b[1;34m(self, positive, negative, topn, restrict_vocab, indexer)\u001b[0m\n\u001b[0;32m    551\u001b[0m                 \u001b[0mmean\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    552\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 553\u001b[1;33m                 \u001b[0mmean\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mweight\u001b[0m \u001b[1;33m*\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mword_vec\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0muse_norm\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    554\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mword\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    555\u001b[0m                     \u001b[0mall_words\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvocab\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mword\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\AppData\\Local\\Programs\\Python\\Python38\\lib\\site-packages\\gensim\\models\\keyedvectors.py\u001b[0m in \u001b[0;36mword_vec\u001b[1;34m(self, word, use_norm)\u001b[0m\n\u001b[0;32m    466\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    467\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 468\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"word '%s' not in vocabulary\"\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    469\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    470\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mget_vector\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mword\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"word 'movie' not in vocabulary\""
     ]
    }
   ],
   "source": [
    "model.wv.most_similar(positive=[\"movie\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "collected-lover",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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